The present invention relates generally to a system for regulating the high pressure component of a transcritical vapor compression system by employing adaptive control.
Traditional vapor compression systems are designed to run at zero superheat, or below the critical temperature of the refrigerant. Vapor compression Systems that operate subcritically are commonly optimized by choosing load characteristics (high side heat rejection), input characteristics (low side heat absorption), and refrigerant characteristics (high/low side refrigerant temperature and superheat).
Chlorine containing refrigerants have been phased out in most of the world as they have a negative effect on ozone. Hydrofluoro carbons (HFCs) have been used as replacement refrigerants, but these refrigerants may also have negative effects. xe2x80x9cNaturalxe2x80x9d refrigerants, such as carbon dioxide and propane, have been proposed as replacement fluids. Carbon dioxide has a low critical point, which causes most air conditioning systems utilizing carbon dioxide to run partially above the critical point, or to run transcritical, under most conditions. The pressure of any subcritical fluid is a function of temperature under saturated conditions (when both liquid and vapor are present). However, when the temperature of the fluid is higher than the critical temperature (supercritical), the pressure becomes a function of the density of the fluid.
When a vapor compression system is run transcritical, it is advantageous to regulate the high pressure component of the system. By regulating the high pressure of the system, the capacity and/or efficiency of the system can be controlled and optimized. By operating the system transcritically, the pressure and the temperature of heat rejection can both be independently controlled.
Adaptive control can be employed to modify variable coefficients in an adaptive control algorithm. By modifying the variable coefficients in the adaptive control algorithm, the optimal variable setpoint reference that achieves maximum capacity can be obtained.
In a prior vapor compression system, receding horizon control using Recursive Least Squares (RLS) has been used for model identification. Systems have also been optimized by adjusting control variables and solving the system through direct matrix inversion. Both of these approaches automatically adjust the variable speed of the heat pump, the blower speed, and the evaporator superheat of a vapor compression system to optimize the coefficient of performance of the system.
A vapor compression system in basic form includes a compressor, a gas cooler, an expansion device, and an evaporator. Refrigerant is circulated though the closed circuit cycle. Carbon dioxide is used as the refrigerant. As carbon dioxide has a low critical point, systems utilizing carbon dioxide as a refrigerant usually require the vapor compression system to run transcritical. When the system is run transcritical, it is advantageous to regulate the high pressure component of the vapor compression system to control and optimize the capacity and/or efficiency of the system. The overall efficiency of the system is determined by comparing the amount of useful energy extracted by the heat rejecting heat exchanger to the amount of energy expended to compress the refrigerant and to run any ancillary components of the system, such as heat exchanger fans or pumps.
As the environment and the system changes over time, the high pressure that provides the maximum coefficient of performance of the system changes. Adaptive control is employed to modify the model that operates the system to continually optimize the coefficient of performance. The model is determined by an adaptive control algorithm including variable coefficients. As the system changes over time, the model that operates the system is modified to optimize the coefficient of performance. As the variables of the adaptive control algorithm change, the model changes. A control of the heat rejecting heat exchanger is then adjusted based on the modifications to regulate the high pressure of the system and therefore the coefficient of performance.
In a first example, Least Mean Squares (LMS) is used to modify the variables of the adaptive control algorithm to optimize the coefficient of performance. In the first step, a system identification error is computed using a gradient descent methodology. In the second step, the model is adapted using the system identification error information. The model is modified such that the output of the model is substantially equal to the output of the system, reducing the system identification error to zero. A control is adjusted based on the adaptive control update to adjust the high pressure of the system in the heat rejecting heat exchanger to obtain the maximum coefficient of performance.
In a second example, the coefficient of performance is optimized by employing a slowly varying periodic excitation method to seek extreme conditions. Intelligent excitation and signal manipulation and filtering are employed to achieve an adaptive control update.
A third example employs triangularization to find the optimal coefficient of performance of the system. A triangle having three setpoints is established: one point is on the left hand side and has a positive slope, one point in on the right hand side and has a negative slope, and a midpoint lies between these points. After first establishing a triangle including three points, the adaptive control algorithm constricts the control variables and focuses on the old midpoint to define a new middle point. Triangularization is repeated until the maximum coefficient of performance is obtained. The system is then run at this input to maximize the coefficient of performance.
These and other features of the present invention will be best understood from the following specification and drawings.